数据处理、pandas常用函数和技巧

1. 通过发卡时间计算年龄

#%% 不同类型卡的持卡人在办卡时的平均年龄对比，issued 为发卡时间，birth_date 为出生日期
import seaborn as sns
import time
import pandas as pd

card_t['age']=(pd.to_datetime(card_t['issued'])-pd.to_datetime(card_t['birth_date']))
card_t['age1']=card_t['age'].map(lambda x:x.days/365)

2. 处理逗号， balance 的数据如 $6,600，处理后为 6600

card_t2['balance2'] = card_t2['balance'].map(lambda x: int(''.join(x[1:].split(','))))

3. 多表连接

card=pd.read_csv(r"card.csv",encoding="gbk")
disp=pd.read_csv(r"disp.csv",encoding="gbk")
clients=pd.read_csv(r"clients.csv",encoding="gbk")

card.to_sql('card', con)
disp.to_sql('disp', con)
clients.to_sql('clients', con)

car_sql='''
select a.*,c.sex,c.birth_date,c.district_id
  from card as a
  left join disp as b on a.disp_id=b.disp_id
  left join clients as c on b.client_id=c.client_id
  where b.type="所有者"

'''

card_t=pd.read_sql(car_sql, con)

4. datatime.timedelta

该函数表示两个时间的间隔

参数可选、默认值都为0：datetime.timedelta(days=0, seconds=0, microseconds=0, milliseconds=0, minutes=0, hours=0, weeks=0)

比如要输出当前时间一小时之后的时间：

#coding:utf-8
from datetime import datetime,timedelta

time1 = datetime.now()

print time1
print time1.strftime("%y-%m-%d %H:%M:%S")
print (time1+timedelta(hours =1)).strftime("%y-%m-%d %H:%M:%S")